ITM Web Conf.
Volume 44, 2022International Conference on Automation, Computing and Communication 2022 (ICACC-2022)
|Number of page(s)||6|
|Published online||05 May 2022|
- Benková, Lucia & Benko, Lubomir. (2020). Neural Machine Translation as a Novel Approach to Machine Translation, (2020). [Google Scholar]
- A. Rathi, “Deep learning apporach for image captioning in Hindi language, ” 2020 International Conference on Computer, Electrical & Communication Engineering (ICCEcE), 2020, pp. 1–8, doi: 10.1109/lCCECE48148.2020.9223087, (2020). [Google Scholar]
- Aspects of Terminological and Named Entity Knowledge within Rule-Based Machine Translation Models for UnderResourced Neural Machine Translation Scenarios. (2020) [Google Scholar]
- B. Premjith & Kumar, M. & Kp, Soman. (2019). Neural Machine Translation System for English to Indian Language Translation Using MTIL Parallel Corpus: Special Issue on Natural Language Processing. Journal of Intelligent Systems. 28. doi: 10.1515/jisys-2019-2510. (2019). [Google Scholar]
- Nair, Jayashree & Krishnan, K. & Deetha, R. (2016). An efficient English to Hindi machine translation system using hybrid mechanism. 2109–2113. doi: 10.1109/ICACCI.2016.7732363. (2016). [Google Scholar]
- P. Anderson, X. He, C. Buehler et al., “Bottom-up and topdown attention for image captioning, ” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, June (2018). [Google Scholar]
- Laskar, Sahinur Rahman et al. “Neural Machine Translation: English to Hindi.” 2019 IEEE Conference on Information and Communication Technology (2019): 1–6. (2019). [Google Scholar]
- JalFaizy Shaikh. “Automatic Image Captioning using Deep Learning (CNN and LsTM) in PyTorch”, (2018). [Google Scholar]
- K. Loganathan, R. Sarath Kumar, V. Nagaraj, Tegil J. John, CNN & LSTM using python for automatic image captioning, Materials Today: Proceedings, 2020, ISSN 2214-7853, doi: 10.1016/j.matpr.2020.10.624. (2020). [Google Scholar]
- https://github.com/manavjain179/Machine-Translation. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.